Deep RL Course documentation

Conclusion

Deep RL Course

Unit 0. Welcome to the course

Unit 1. Introduction to Deep Reinforcement Learning

Bonus Unit 1. Introduction to Deep Reinforcement Learning with Huggy

Live 1. How the course work, Q&A, and playing with Huggy

Unit 2. Introduction to Q-Learning

Unit 3. Deep Q-Learning with Atari Games

Bonus Unit 2. Automatic Hyperparameter Tuning with Optuna

Unit 4. Policy Gradient with PyTorch

Unit 5. Introduction to Unity ML-Agents

Introduction How ML-Agents works? The SnowballTarget environment The Pyramids environment (Optional) What is curiosity in Deep Reinforcement Learning? Hands-on Bonus. Learn to create your own environments with Unity and MLAgents Quiz Conclusion

Unit 6. Actor Critic methods with Robotics environments

Unit 7. Introduction to Multi-Agents and AI vs AI

Unit 8. Part 1 Proximal Policy Optimization (PPO)

Unit 8. Part 2 Proximal Policy Optimization (PPO) with Doom

Bonus Unit 3. Advanced Topics in Reinforcement Learning

Certification and congratulations

Join the Hugging Face community

and get access to the augmented documentation experience

Collaborate on models, datasets and Spaces

Faster examples with accelerated inference

Switch between documentation themes

to get started

Conclusion

Congrats on finishing this unit! You’ve just trained your first ML-Agents and shared it to the Hub 🥳.

The best way to learn is to practice and try stuff. Why not try another environment? ML-Agents has 18 different environments.

For instance:

Worm, where you teach a worm to crawl.
Walker, where you teach an agent to walk towards a goal.

Check the documentation to find out how to train them and to see the list of already integrated MLAgents environments on the Hub: https://github.com/huggingface/ml-agents#getting-started

Example envs

In the next unit, we’re going to learn about multi-agents. You’re going to train your first multi-agents to compete in Soccer and Snowball fight against other classmate’s agents.

Snownball fight

Finally, we would love to hear what you think of the course and how we can improve it. If you have some feedback then please 👉 fill this form

Keep Learning, stay awesome 🤗

< > Update on GitHub

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